| Summary: | Early detection of the pulmonary nodule is critical to increase the five-year survival rate of lung cancer. Many computer-aided diagnosis (CAD) systems have been proposed for nodule detection to assist radiologists in diagnosis. Along this direction, this paper proposes a novel automated pulmonary nodule detection model using the modified V-Nets and a high-level descriptor based support vector machine (SVM) classifier. The former is for nodule candidate detection and the latter is for false positive (FP) reduction. A hard mining scheme for retraining is devised to improve the FP reduction performance. The proposed SVM classifier, which employs more critical features of CT images, performs superior in FP reduction than other SVM based classifiers and CNN classifiers. Experimental results using the LIDC-IDRI dataset are presented to demonstrate the effectiveness of the proposed CAD model.
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